Staff Product Engineer, Data & AI

Prokeep is building the future of order automation for distributors and we're hiring a Staff Product Engineer, Data/AI to build the data and intelligence foundation that the next generation of Prokeep is built on. This role exists because the hardest problems we're solving aren't solved by writing more code faster — they're solved by understanding the shape of messy, real-world data and designing systems that make it tractable. Prokeep sits on a large, rich, and deeply messy dataset: customer communications, order history, product catalogs, and interaction signals — much of it generated by our customers in ways we don't control, and shaped by industry conventions that vary widely across our verticals. Turning that into something a product can reliably reason over is the central engineering challenge of this role. You'll work across the stack — data, backend, and product — to take heterogeneous, sparse, inconsistent inputs and build systems that produce structured, verifiably correct outputs at scale. AI is part of the toolkit. LLMs, embeddings, and retrieval are useful where they fit; classical techniques, deterministic logic, and good old-fashioned data modeling are often the better answer. Pragmatic judgment about which tool to reach for is core to the job. This isn't a research role or a prototyping playground. You'll own systems end-to-end: from understanding the problem, to shaping the data, to shipping the product surface, to running it reliably in production. Primary Responsibilities

• Take ownership of messy, heterogeneous, customer-generated data and design the systems, structures, and pipelines that turn it into a reliable foundation for product features. - Build systems that produce verifiably correct, structured outputs from ambiguous inputs — including the eval frameworks and ground-truth datasets that prove they actually work. - Ship customer-facing features end-to-end on top of that foundation, working across backend, data, and (where needed) frontend. - Make pragmatic decisions about when to reach for an LLM, when to use embeddings or retrieval, when classical ML is the right call, and when the answer is just well-modeled data and deterministic logic. - Own the quality and reliability of intelligent systems in production: latency, cost, fallback logic, monitoring, and continuous evaluation against ground truth. - Help shape Prokeep's strategy for where intelligence should live in the product, what to prioritize, and what tradeoffs to make. - Partner closely with our Data Engineer to surface and design around data quality issues, and with Product to ensure the systems we build actually move the business. - Improve systems already in production while contributing to greenfield initiatives — and over time, help us move from generalist LLMs toward custom models trained on our own data as it accumulates. Qualifications (About You)

• 6+ years building and shipping production systems, with depth in backend and data work. Full-stack range is a plus. - A track record of taking messy, real-world data — sparse, inconsistent, shaped by inputs you don't control — and turning it into something a system can reason over reliably. You know how to profile, align, normalize, and structure it, and you don't get overwhelmed by scale or chaos. - Experience building systems where correctness is verifiable and measurable — search relevance, entity resolution, record linkage, catalog or taxonomy systems, data integration, or similar problems where there's a right answer and you have to prove you got it. - Comfort applying LLMs and embeddings where they're the right tool, and equal comfort using simpler, more deterministic approaches when they aren't. You've shipped features that used AI in production and you can speak honestly about where it helped and where it didn't. - Strong fundamentals in APIs, system design, data flow, and failure handling. - Pragmatic judgment about scope: how to limit a 45,000-row catalog to the 5,000 rows that actually matter, how to handle the long tail of edge cases, how to know when something is good enough to ship. - A strong product and customer mindset — you think in workflows and outcomes, not just systems. - Willingness to work in Elixir (our primary backend); strong experience with Python expected. Familiarity with React and PostgreSQL is a plus.